Methods and systems for maintaining hygienic conditions in automatic teller machines

ABSTRACT

Methods and systems are described for maintaining hygienic conditions in automatic teller machines by detecting whether or not a user is not in compliance with a hygienic standard. If a user is not in compliance, then the automatic teller machine may execute a hygienic action to cleanse the automatic teller machine. For example, the hygienic action may comprise automatically cleansing the automatic teller machine, disabling the automatic teller machine from service, transmitting a sanitation service request to an automatic teller machine provide, and/or initiating an alternative control scheme (e.g., voice controls, gesture-based controls, etc.) for the automatic teller machine.

FIELD OF THE INVENTION

The invention relates to for maintaining hygienic conditions inautomatic teller machines.

BACKGROUND

In recent years, there has been an increased focus on maintaininghygienic conditions. This focus has only intensified in view of theCOVID-19 pandemic of 2020. Maintaining hygienic conditions is howeverparticularly difficult in situations in which multiple users must usethe same device. The problem is further exacerbated if the devicerequires a user to physically touch the device and stand close to thedevice. An automatic teller machine is this type of device. In additionto requiring a user to touch the automatic teller machine, users oftenstand very close to the automatic teller machine for security reasons.

SUMMARY

Methods and systems are described herein for maintaining hygienicconditions in automatic teller machines. More specifically, methods andsystem are described herein for maintaining hygienic conditions inautomatic teller machines by detecting whether or not a user is not incompliance with a hygienic standard. If a user is not in compliance,then the automatic teller machine may execute a hygienic action tocleanse the automatic teller machine. For example, the hygienic actionmay comprise automatically cleansing the automatic teller machine,disabling the automatic teller machine from service, transmitting asanitation service request to an automatic teller machine provider,and/or initiating an alternative control scheme (e.g., voice controls,gesture-based controls, etc.) for the automatic teller machine.

To determine whether or not to cleanse the automatic teller machine thesystems and methods may capture an image of a user at the automaticteller machine using a camera in the automatic teller machine andprocess the image using a facial recognition model to identify whetheror not the user is wearing a mask. More specifically, the system may usea facial recognition model to identify a nose and a mouth of the user.If the nose and mouth cannot be identified (e.g., because they arecovered by a mask), the system determines that the user is user is incompliance with a hygienic standard. For example, by using a facialrecognition model to identify the nose and mouth of a user (e.g., asopposed to using a model trained to identify whether or not a user iswearing a mask), the system may determine a user is not in compliancewith a hygienic standard if he/she is wearing a mask, but wearing itimproperly (e.g., wearing it around his/her neck or otherwise not havingit cover the nose and mouth of the user).

Furthermore, by having the image captured by a camera in the automaticteller machine and using a facial recognition model that is trained onimages of users at automatic teller machines the accuracy of theidentification may be improved. For example, automatic teller machinesare outfitted with a camera, and the camera is positioned such that anyuser of the automatic teller machine is necessarily caught on cameraand/or subject to the lighting of the automatic teller machine (e.g.,glint detected in the eye of a user based on the light may aid indetecting the eyes of a user). Nonetheless, because of the close range,specialized lighting condition, sharp angle, and/or specialized lens(e.g., wide angle lens) used in the camera of an automatic tellermachine, an image of a user at an automatic teller machine may appeardistorted. By training the facial recognition model on these images asopposed to stock profile images and/or images caught with conventionalcameras, the system may account for the distortions and improveaccuracy.

In some aspects, a system and method for maintaining hygienic conditionsin automatic teller machines is disclosed. For example, the system mayreceive a user request to initiate a session with an automatic tellermachine. In response to receiving the user request, the system maycapture an image of a user at the automatic teller machine. The systemmay process the image using a facial recognition model to identify anose and a mouth of the user. In response to identifying the nose ormouth in the image, the system may determine that the user is not incompliance with a hygienic standard and execute a hygienic action tocleanse the automatic teller machine.

Various other aspects, features, and advantages of the invention will beapparent through the detailed description of the invention and thedrawings attached hereto. It is also to be understood that both theforegoing general description and the following detailed description areexamples and not restrictive of the scope of the invention. As used inthe specification and in the claims, the singular forms of “a,” “an,”and “the” include plural referents unless the context clearly dictatesotherwise. In addition, as used in the specification and the claims, theterm “or” means “and/or” unless the context clearly dictates otherwise.Additionally, as used in the specification “a portion,” refers to a partof, or the entirety of (i.e., the entire portion), a given item (e.g.,data) unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative system features an automatic teller machineand mobile device, in accordance with one or more embodiments.

FIG. 2 shows an illustrative system for maintaining hygienic conditionsin automatic teller machines, in accordance with one or moreembodiments.

FIG. 3 shows an illustrative system for detecting whether or not a useris in compliance with a hygienic standard in automatic teller machines,in accordance with one or more embodiments.

FIG. 4 shows a flowchart of the steps involved in maintaining hygienicconditions in automatic teller machines, in accordance with one or moreembodiments.

FIG. 5 shows a flowchart of the steps involved in implementing analternative control scheme in response to determining a user is not incompliance with a hygienic standard, in accordance with one or moreembodiments.

FIG. 6 shows a flowchart of the steps involved in training a machinelearning model to determine whether or not a user is in compliance witha hygienic standard, in accordance with one or more embodiments.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It will beappreciated, however, by those having skill in the art, that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other cases, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the embodiments of the invention. It should alsobe noted that the methods and systems disclosed herein are also suitablefor applications unrelated to source code programming.

FIG. 1 shows an illustrative system featuring an automatic tellermachine and mobile device, in accordance with one or more embodiments.For example, FIG. 1 shows system 100. System 100 includes automaticteller machine 102 and secondary device 150. Automatic teller machine102 may include display 106 whereas secondary device 150 may includedisplay 152. Information used to conduct transactions using system 100may appear on display 106 and display 152. System 100 may include camera108, which may be positioned to capture images of a user at theautomatic teller machine (e.g., a user using keypad 104).

For example, automatic teller machine 102 may be an electronic bankingoutlet that allows a user to complete basic transactions without the aidof a branch representative or teller. The automatic teller machine 102may allow any user with a credit card or debit card to receive cashand/or perform financial actions such as deposits, cash withdrawals,bill payments, and transfers between accounts.

In some embodiments, automatic teller machine 102 and secondary device150 may initiate a temporary device session in order to performfinancial transactions. For example, the temporary device session may beinitiated and maintained through the exchange of QR code and/or otherimage-based and/or electrically communicated codes.

In some embodiments, the automatic teller machine 102 may beself-cleaning. For example, the automatic teller machine 102 may includedisinfecting means and/or provide means for disinfecting automaticteller machine 102. For example, the automatic teller machine 102 maygenerate a notification (e.g., on display 106) asking the user to cleanautomatic teller machine 102, may automatically clean the automaticteller machine 102, request (e.g., to a provider of the automatic tellermachine 102) a cleaning of automatic teller machine 102, and/or disableautomatic teller machine 102 until a hygienic standard (e.g., a timelimit between users) is compliant.

As referred to herein, a hygienic standard may be defined by hygienepractices related to the administration of medicine and medical careand/or use of the automatic teller machine 102 that prevents orminimizes the spread of disease. In some embodiments, the hygienicstandard may be based on respiratory hygiene, which may relate to covera nose and mouth of a user. In some embodiments, the hygienic standardmay be based on the user of protective coverings of a hand and/or face.

FIG. 2 shows an illustrative system for maintaining hygienic conditionsin automatic teller machines, in accordance with one or moreembodiments. As shown in FIG. 2, system 200 may include user device 222,user device 224, and/or other components. Each user device may includeany type of mobile terminal, fixed terminal, or other device. Forexample, user device 224 may comprise an automatic teller machine (e.g.,automatic teller machine 102 (FIG. 1)). For example, each of thesedevices may comprise one or more of the devices shown in FIG. 1. Each ofthese devices may receive content and data via input/output (hereinafter“I/O”) paths and may also include processors and/or control circuitry tosend and receive commands, requests, and other suitable data using theI/O paths. The control circuitry may be comprised of any suitableprocessing circuitry. Each of these devices may also include a userinput interface and/or display for use in receiving and displaying data(e.g., user interface 102 (FIG. 1)).

By way of example, user device 222 may include a desktop computer, aserver, or other client device. Users may, for instance, utilize one ormore of the user devices to interact with one another, one or moreservers, or other components of system 200. It should be noted that,while one or more operations are described herein as being performed byparticular components of system 200, those operations may, in someembodiments, be performed by other components of system 200. As anexample, while one or more operations are described herein as beingperformed by components of user device 222, those operations may, insome embodiments, be performed by components of user device 224. System200 also includes machine learning model 202, which may be implementedon user device 222, user device 224, and/or a cloud-based system, and/oraccessible by communication paths 228 and 230, respectively. It shouldbe noted that, although some embodiments are described herein withrespect to machine learning models, other prediction models (e.g.,statistical models or other analytics models) may be used in lieu of, orin addition to, machine learning models in other embodiments (e.g., astatistical model replacing a machine learning model and anon-statistical model replacing a non-machine learning model in one ormore embodiments).

Each of these devices may also include memory in the form of electronicstorage. The electronic storage may include non-transitory storage mediathat electronically stores information. The electronic storage of mediamay include (i) system storage that is provided integrally (e.g.,substantially non-removable) with servers or client devices and/or (ii)removable storage that is removably connectable to the servers or clientdevices via, for example, a port (e.g., a USB port, a firewire port,etc.) or a drive (e.g., a disk drive, etc.). The electronic storages mayinclude optically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. The electronicstorages may include virtual storage resources (e.g., cloud storage, avirtual private network, and/or other virtual storage resources). Theelectronic storage may store software algorithms, information determinedby the processors, information obtained from servers, informationobtained from client devices, or other information that enables thefunctionality as described herein.

FIG. 2 also includes communication paths 228, 230, and 232.Communication paths 228, 230, and 232 may include the Internet, a mobilephone network, a mobile voice or data network (e.g., a 4G or LTEnetwork), a cable network, a public switched telephone network, or othertypes of communications network or combinations of communicationsnetworks. Communication paths 228, 230, and 232 may include one or morecommunications paths, such as a satellite path, a fiber-optic path, acable path, a path that supports Internet communications (e.g., IPTV),free-space connections (e.g., for broadcast or other wireless signals),or any other suitable wired or wireless communications path orcombination of such paths. The computing devices may include additionalcommunication paths linking a plurality of hardware, software, and/orfirmware components operating together. For example, the computingdevices may be implemented by a cloud of computing platforms operatingtogether as the computing devices.

As an example, with respect to FIG. 2, machine learning model 202 maytake inputs 204 and provide outputs 206. For example, machine learningmodel 202 may be used by an automatic teller machine (e.g., automaticteller machine 102 (FIG. 1)) and/or a financial services provider toreceive, from a user device of a user, a user request to access theautomatic teller machine. Machine learning model 202 may then be used byan automatic teller machine (e.g., automatic teller machine 102 (FIG.1)) to generate for display, on the user device, a recommendation forthe location from a subset at which to access the service at a giventime interval. For example, machine learning model 202 may be used toefficiently process information on available services, sanitationconditions, and assessments of user compliance with hygienic standardsin order to provide real-time recommendations.

The inputs may include multiple data sets such as a training data setand a test data set. In some embodiments, outputs 206 may be fed back tomachine learning model 202 as input to train machine learning model 202(e.g., alone or in conjunction with user indications of the accuracy ofoutputs 206, labels associated with the inputs, or with other referencefeedback information). In another embodiment, machine learning model 202may update its configurations (e.g., weights, biases, or otherparameters) based on the assessment of its prediction (e.g., outputs206) and reference feedback information (e.g., user indication ofaccuracy, reference labels, or other information). In anotherembodiment, where machine learning model 202 is a neural network,connection weights may be adjusted to reconcile differences between theneural network's prediction and the reference feedback. In a further usecase, one or more neurons (or nodes) of the neural network may requirethat their respective errors are sent backward through the neuralnetwork to them to facilitate the update process (e.g., backpropagationof error). Updates to the connection weights may, for example, bereflective of the magnitude of error propagated backward after a forwardpass has been completed. In this way, for example, the machine learningmodel 202 may be trained to generate better predictions.

In some embodiments, machine learning model 202 may include anartificial neural network. In such embodiments, machine learning model202 may include an input layer and one or more hidden layers. Eachneural unit of machine learning model 202 may be connected with manyother neural units of machine learning model 202. Such connections canbe enforcing or inhibitory in their effect on the activation state ofconnected neural units. In some embodiments, each individual neural unitmay have a summation function which combines the values of all of itsinputs together. In some embodiments, each connection (or the neuralunit itself) may have a threshold function such that the signal mustsurpass before it propagates to other neural units. Machine learningmodel 202 may be self-learning and trained, rather than explicitlyprogrammed, and can perform significantly better in certain areas ofproblem solving, as compared to traditional computer programs. Duringtraining, an output layer of machine learning model 202 may correspondto a classification of machine learning model 202 and an input known tocorrespond to that classification may be input into an input layer ofmachine learning model 202 during training. During testing, an inputwithout a known classification (e.g., whether or not a user is wearing amask, whether or not the nose or mouth of a user is identified, and/or adegree to which a hygienic condition is complied with) may be input intothe input layer, and a determined classification may be output.

In some embodiments, machine learning model 202 may include multiplelayers (e.g., where a signal path traverses from front layers to backlayers). In some embodiments, back propagation techniques may beutilized by machine learning model 202 where forward stimulation is usedto reset weights on the “front” neural units. In some embodiments,stimulation and inhibition for machine learning model 202 may be morefree-flowing, with connections interacting in a more chaotic and complexfashion. During testing, an output layer of machine learning model 202may indicate whether or not a given input corresponds to aclassification of machine learning model 202).

FIG. 3 shows an illustrative system for detecting whether or not a useris in compliance with a hygienic standard in automatic teller machines,in accordance with one or more embodiments. FIG. 3 shows automaticteller machine camera system 300 and automatic teller machine camerasystem 350, which may be used to identify whether or not a user is incompliance with a hygienic standard. Automatic teller machine camerasystem 300 and automatic teller machine camera system 350 may includeautomatic teller machine control circuitry, an automatic teller machinelight source, and an automatic teller machine camera. Automatic tellermachine light source transmits light that reaches at least one eye of auser, and automatic teller machine camera is directed at the user tosense reflected light. Automatic teller machine camera may transmitcollected data to automatic teller machine control circuitry and basedon the data received from automatic teller machine camera, automaticteller machine control circuitry may identify whether or not a user isin compliance with hygienic standards. For example, automatic tellermachine camera system 300 may illustrate an example where a user is incompliance with a hygienic standard. Automatic teller machine camerasystem 350 may illustrate an example where a user is in compliance witha hygienic standard.

In some embodiments, automatic teller machine camera system 300 isconfigured for identifying facial features of a user. For example, thesystem may identify a facial feature and a distance between facialfeatures. Furthermore, automatic teller machine control circuitry may beintegrated with one or more automatic teller machine light sources andone or more automatic teller machine cameras in order to generate imagesspecific to users using automatic teller machines. Additionally oralternatively, one or more automatic teller machine light sources andone or more automatic teller machine cameras may be housed separatelyfrom automatic teller machine control circuitry and in wireless or wiredcommunication with automatic teller machine control circuitry.

Automatic teller machine light source transmits light to one or botheyes of one or more users. Automatic teller machine light source mayemit, for example, infrared (IR) light, near infrared light, or visiblelight. The light emitted by automatic teller machine light source may becollimated or non-collimated. The light is reflected in a user's eye,forming, for example, the reflection from the outer surface of thecornea (i.e., a first Purkinje image), the reflection from the innersurface of the cornea (i.e., a second Purkinje image), the reflectionfrom the outer (anterior) surface of the lens (i.e., a third Purkinjeimage), and/or the reflection from the inner (posterior) surface of thelens (i.e., a fourth Purkinje image). Moreover, an automatic tellermachine camera may be configured to capture images based on thespecialized light source of the automatic teller machine.

Automatic teller machine camera collects visual information, such as animage or series of images, of one or both of one or more users' eyes.Automatic teller machine camera transmits the collected image(s) toautomatic teller machine control circuitry, which processes the receivedimage(s) to identify a glint (i.e., corneal reflection) and/or otherreflection in one or both eyes of one or more users. Automatic tellermachine control circuitry may also determine the location of the centerof the pupil of one or both eyes of one or more users. For each eye,automatic teller machine control circuitry may compare the location ofthe pupil to the location of the glint and/or other reflection toestimate the gaze point. Automatic teller machine control circuitry mayalso store or obtain information describing the location of one or moreautomatic teller machine light sources and/or the location of one ormore automatic teller machine cameras relative to a display (e.g.,display 106 (FIG. 1)). Using this information, automatic teller machinecontrol circuitry may determine a likely position of a user's eyes basedon the glint. Once the system determines a position of the user's eyes,the system may attempt to identify other facial features (e.g., a noseand a mouth).

In some embodiments, automatic teller machine camera system 300 isconfigured to account for a user's head movement and/or the position ofa user's head while using the automatic teller machine. In someembodiments accounting for a user's head position, automatic tellermachine camera system 300 includes two or more automatic teller machinecameras. For example, two cameras may be arranged to form a stereovision system for obtaining a 3D position of the user's eye or eyes.Furthermore, multiple cameras may allow the automatic teller machinecontrol circuitry to compensate for head movement when determining theuser's gaze point. The two or more automatic teller machine cameras maybe part of a single unit or may be separate units. For example, anautomatic teller machine camera system 300 in communication with theuser device (e.g., user device 222 (FIG. 2)) may include two automaticteller machine cameras. In other embodiments, each user device (e.g.,user device 222 (FIG. 2)) and automatic teller machine camera system 300may include an optical sensor, and automatic teller machine controlcircuitry receives image data from the optical sensor of the user deviceand the optical sensor of automatic teller machine camera system 300.

In other embodiments accounting for a user's head movement, automaticteller machine camera system 300 includes two or more light sources forgenerating multiple glints. For example, two automatic teller machinelight sources may create glints at different locations of an eye.Automatic teller machine control circuitry may also receive dataidentifying the location of automatic teller machine light sourcesrelative to a display (e.g., display 106 (FIG. 1)) and/or adjust anautomatic teller machine camera system 300.

FIG. 4 shows a flowchart of the steps involved in maintaining hygienicconditions in automatic teller machines, in accordance with one or moreembodiments. In some embodiments, process 400 may be performed by one ormore components of the systems shown in FIGS. 1-3.

At step 402, process 400 receives (e.g., via one or more components ofFIGS. 1-2) a user request to initiate a session with an automatic tellermachine. For example, the system may receive a user request (e.g., viainputs to the automatic teller machine, based on the automatic tellermachine detecting a user's approach, etc.) to initiate a session with anautomatic teller machine. For example, the system may, in response toreceiving the user request, verify an identify of user at the automaticteller machine. The system may determine a user account associated withthe user. In response to determining a user account (e.g., a bankaccount number of other number associated with a financial servicesprovider and/or the automatic teller machine).

At step 404, process 400 captures (e.g., via one or more components ofFIGS. 1-2) an image of a user at the automatic teller machine. Forexample, the system may capture an image of a user at the automaticteller machine in response to receiving the user request. For example,as described in FIG. 3, the system may capture an image of the user atthe automatic teller machine using a camera in the automatic tellermachine. For example, automatic teller machines are outfitted with acamera, and the camera is positioned such that any user of the automaticteller machine is necessarily caught on camera and/or subject to thelighting of the automatic teller machine (e.g., glint detected in theeye of a used based on the light may aid in detecting the eyes of auser).

At step 406, process 400 processes (e.g., via one or more components ofFIGS. 1-2) the image using facial recognition algorithm to identify anose and a mouth of the user. For example, the system may process theimage using facial recognition model to identify a nose and a mouth ofthe user. For example, the facial recognition model (as described inFIG. 2 above) may comprise a machine learning model trained to identifynoses and faces in images. For example, the machine learning model isfurther trained to identify whether users are wearing masks (e.g., basedon the noses and faces in images). Furthermore, by training the facialrecognition model on these images as opposed to stock profile imagesand/or images caught with conventional cameras, the system may accountfor the distortions in the images as a result of the camera and improveaccuracy.

At step 408, process 400 determines (e.g., via one or more components ofFIGS. 1-2) a user is not in compliance with a hygienic standard. Forexample, the system may, in response to identifying the nose or mouth inthe image, determine that the user is not in compliance with a hygienicstandard. For example, the system may detect eye glint of a user (e.g.,as described in FIG. 3), nose, and/or other facial features of a user.In some embodiments, the system may determine a degree of compliancewith the hygienic standard and/or select a hygienic action based on thedegree of compliance.

At step 410, process 400 executes (e.g., via one or more components ofFIGS. 1-2) a hygienic action to cleanse the automatic teller machine.For example, the system may, in response to identifying the nose ormouth in the image, execute a hygienic action to cleanse the automaticteller machine. In contrast, in response to not identifying the nose ormouth in the image, the system may determine that the user is incompliance with the hygienic standard.

For example, the hygienic action may comprise initiating an ultravioletsanitation procedure on the automatic teller machine. For example, theautomatic teller machine may include an ultraviolet germicidalirradiation device incorporated into the automatic teller machine. Thedevice may use short-wavelength ultraviolet (ultraviolet C or UV-C)light to kill or inactivate microorganisms by destroying nucleic acidsand disrupting their DNA, leaving them unable to perform vital cellularfunctions. In some embodiments, the device may be a mercury-vapor lampthat has a strong emission line at 254-290 nm, which is within the rangeof wavelengths that demonstrate strong disinfection effect.

Additionally or alternatively, the hygienic action comprises disablingthe automatic teller machine from service and/or generating for display,on a display screen of the automatic teller machine, a notification touse an alternative automatic teller machine. For example, the system maygenerate a notification that may include directions to another automaticteller machine and/or a time when the automatic teller machine with besanitized and/or enabled. For example, the hygienic action may compriseof transmitting a sanitation service request to an automatic tellermachine provider and/or the automatic teller machine may remain disableduntil the automatic teller machine is sanitized.

It is contemplated that the steps or descriptions of FIG. 4 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 4 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order or inparallel or substantially simultaneously to reduce lag or increase thespeed of the system or method. Furthermore, it should be noted that anyof the devices or equipment discussed in relation to FIGS. 1-3 could beused to perform one or more of the steps in FIG. 4.

FIG. 5 shows a flowchart of the steps involved in implementing analternative control scheme in response to determining a user is not incompliance with a hygienic standard, in accordance with one or moreembodiments. In some embodiments, process 500 may be performed by one ormore components of the systems shown in FIGS. 1-3.

At step 502, process 500 determines (e.g., via one or more components ofFIGS. 1-2) an automatic teller machine is not in compliance with ahygienic standard. For example, the system may determine that a user isnot wearing a mask (e.g., the system may identify a nose or mouth in animage of a user at an automatic teller machine.

At step 504, process 500 generates (e.g., via one or more components ofFIGS. 1-2) for display a request to use an alternative control scheme.For example, the system may execute a hygienic action in response todetermining a user is not wearing a mask. In some embodiments, thealternative control scheme may comprise gesture-based control or voiceactivated controls. For example, the system may display (or present viaan audio output) a request to user gesture-based control or a voiceactivated controls. In some embodiments, the alternative control schememay comprise controlling the automatic teller machine via a secondarydevice. For example, the system may display (on a user interface of thesecondary device) a request to use the secondary device to control theautomatic teller machine. Additionally, the system may issue a pairingrequest with the secondary device and/or initiate another device sessionbetween the automatic teller machine and the secondary device.

At step 506, process 500 receives (e.g., via one or more components ofFIGS. 1-2) selection of alternative control scheme. For example, thehygienic action may comprise initiating an alternative control schemefor the automatic teller machine. In some embodiments, the alternativecontrol scheme may comprise gesture-based control or voice activatedcontrols. For example, the system may capture a gesture from a userand/or receive a voice command from the user and in response the systemmay select the alternative control scheme. In some embodiments, thealternative control scheme may comprise controlling the automatic tellermachine via a secondary device. For example, the system may receive acontrol command or acceptance of the alternative control scheme from thesecondary device.

At step 508, process 500 receives (e.g., via one or more components ofFIGS. 1-2) user inputs via alternative control scheme. For example, thesystem may continue to receive user inputs using the selectedalternative control scheme. For example, for gesture-based controls, thecamera of the automatic teller machine may be a depth-aware camera thatgenerates a depth map of what is being seen through the camera at ashort range, and use this data to approximate a three dimensionrepresentation of what is being seen. These can be effective fordetection of hand gestures due to their short range capabilities such asAmerican signal language gestures and/or other hand gestures. In someembodiments, the system may include multiple cameras that detect threedimensional relationships to the images created by the multiple cameras.In some embodiments, the system may also use one or more threedimensional model-based algorithms.

It is contemplated that the steps or descriptions of FIG. 5 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 5 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order or inparallel or substantially simultaneously to reduce lag or increase thespeed of the system or method. Furthermore, it should be noted that anyof the devices or equipment discussed in relation to FIGS. 1-3 could beused to perform one or more of the steps in FIG. 5.

FIG. 6 shows a flowchart of the steps involved in training a machinelearning model (e.g., machine learning model 202 (FIG. 2)) to determinewhether or not a user is in compliance with a hygienic standard, inaccordance with one or more embodiments. For example, FIG. 6 describesprocess 600 for determining whether or not a user of an automatic tellermachine is wearing a mask.

At step 602, process 600 selects data sets from available training data.For example, the system may be trained based on images of users at anautomatic teller machine. By training the machine learning model onthese images as opposed to stock profile images and/or images caughtwith conventional cameras, the system may account for any distortionsattributable to the close range, specialized lighting conditions, sharpangles, and/or specialized lens (e.g., wide angle lens) used in a cameraof an automatic teller machine—thus improving accuracy.

At step 604, process 600 prepares sets of images. For example, thesystem may prepare the data for training the machine learning model. Forexample, the system may randomize a first training characteristic (e.g.,a given lighting condition) or control for the first trainingcharacteristic. For example, the first training characteristic may be asetting or characteristic specific to a camera used to capture the firsttraining set of images.

At step 606, process 600 trains a machine learning model using a firsttraining set of images comprising users at automatic teller machine withcorresponding labels for a trait (e.g., whether a nose or mouth ispresent, whether a user is wearing gloves, whether or not a user iswearing a mask, etc.). The system may train the machine learning modelto detect one or more traits, which may include any characteristic thatmay distinguish one classification from another.

In some embodiments, the machine learning model may be a convolutionalneural network. The convolutional neural network is an artificial neuralnetwork that features one or more convolutional layers. Convolutionlayers extract features from an input image. Convolution preserves therelationship between pixels by learning image features using smallsquares of input data. For example, the relationship between theindividual parts of a face of a user (e.g., eyes, nose, moth, etc.) maybe preserved. In another example, the relationship between a glint of aneye (e.g., as discussed in FIG. 3) may be preserved to identify an eyeof a user and its relationship to other facial features of the user.

It is contemplated that the steps or descriptions of FIG. 6 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 6 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order or inparallel or substantially simultaneously to reduce lag or increase thespeed of the system or method. Furthermore, it should be noted that anyof the devices or equipment discussed in relation to FIGS. 1-3 could beused to perform one or more of the steps in FIG. 6.

The above-described embodiments of the present disclosure are presentedfor purposes of illustration and not of limitation, and the presentdisclosure is limited only by the claims which follow. Furthermore, itshould be noted that the features and limitations described in any oneembodiment may be applied to any other embodiment herein, and flowchartsor examples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real time. It should also be noted that the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

-   1. A method for maintaining hygienic conditions in automatic teller    machines, the method comprising: receiving a user request to    initiate a session with an automatic teller machine; in response to    receiving the user request, capturing an image of a user at the    automatic teller machine; processing the image using a facial    recognition model to identify a nose and a mouth of the user; in    response to identifying the nose or mouth in the image: determining    that the user is not in compliance with a hygienic standard; and    executing a hygienic action to cleanse the automatic teller machine.-   2. The method of embodiment 1, wherein the hygienic action comprises    initiating an ultraviolet sanitation procedure on the automatic    teller machine.-   3. The method of any of the preceding embodiments, wherein the    hygienic action comprises generating for display, on a display    screen of the automatic teller machine, a notification to use an    alternative automatic teller machine.-   4. The method of any of the preceding embodiments, wherein the    hygienic action comprises disabling the automatic teller machine    from service.-   5. The method of any of the preceding embodiments, wherein the    hygienic action comprises transmitting a sanitation service request    to an automatic teller machine provider.-   6. The method of any of the preceding embodiments, wherein the    hygienic action comprises initiating an alternative control scheme    for the automatic teller machine, and wherein the alternative    control scheme comprises gesture-based control or voice activated    controls.-   7. The method of any of the preceding embodiments, wherein the    hygienic action comprises initiating an alternative control scheme    for the automatic teller machine, and wherein initiating the    alternative control scheme comprises: generating for display, on a    display screen of the automatic teller machine, a request to user a    secondary input device; initiating a device session with the    secondary device; and receiving user inputs via the secondary    device.-   8. The method of any of the preceding embodiments, further    comprising: in response to receiving the user request, verifying an    identify of user at the automatic teller machine; and determining a    user account associated with the user.-   9. The method of any of the preceding embodiments, wherein the    facial recognition model comprises a machine learning model trained    to identify noses and faces in images.-   10. The method of embodiment 9, wherein the machine learning model    is further trained to identify whether users are wearing masks.-   11. The method of any of the preceding embodiments further    comprising determining that the user is in compliance with the    hygienic standard in response to not identifying the nose or mouth    in the image.-   11. A tangible, non-transitory, machine-readable medium storing    instructions that, when executed by a data processing apparatus,    cause the data processing apparatus to perform operations comprising    those of any of embodiments 1-10.-   12. A system comprising: one or more processors; and memory storing    instructions that, when executed by the processors, cause the    processors to effectuate operations comprising those of any of    embodiments 1-10.-   13. A system comprising means for performing any of embodiments    1-10.

What is claimed is:
 1. A system for maintaining hygienic conditions inautomatic teller machines, the system comprising: an automatic tellermachine; a camera at the automatic teller machine configured to captureimages of users; and control circuitry configured to: receive a userrequest to initiate a session with an automatic teller machine; verifyan identify of user at the automatic teller machine in response toreceiving the user request; determine whether the verified user is incompliance with a hygienic standard by: capturing an image of a user atthe automatic teller machine using the camera; processing the imageusing facial recognition model to identify a nose and a mouth of theuser, wherein the facial recognition model is trained based on images ofusers at an automatic teller machine; determine, in response toidentifying the nose or mouth in the image, that the user is not incompliance with the hygienic standard and execute a hygienic action tocleanse the automatic teller machine; and determine, in response to notidentifying the nose or mouth in the image, that the user is incompliance with the hygienic standard.
 2. A method for maintaininghygienic conditions in automatic teller machines, the method comprising:receiving a user request to initiate a session with an automatic tellermachine; in response to receiving the user request, capturing an imageof a user at the automatic teller machine; processing the image using afacial recognition model to identify a nose and a mouth of the user; inresponse to identifying the nose or mouth in the image: determining thatthe user is not in compliance with a hygienic standard; and executing ahygienic action to cleanse the automatic teller machine; and in responseto not identifying the nose or mouth in the image, determining that theuser is in compliance with the hygienic standard.
 3. The method of claim2, wherein the hygienic action comprises initiating an ultravioletsanitation procedure on the automatic teller machine.
 4. The method ofclaim 2, wherein the hygienic action comprises generating for display,on a display screen of the automatic teller machine, a notification touse an alternative automatic teller machine.
 5. The method of claim 2,wherein the hygienic action comprises disabling the automatic tellermachine from service.
 6. The method of claim 2, wherein the hygienicaction comprises transmitting a sanitation service request to anautomatic teller machine provider.
 7. The method of claim 2, wherein thehygienic action comprises initiating an alternative control scheme forthe automatic teller machine, and wherein the alternative control schemecomprises gesture-based control or voice activated controls.
 8. Themethod of claim 2, wherein the hygienic action comprises initiating analternative control scheme for the automatic teller machine, and whereininitiating the alternative control scheme comprises: generating fordisplay, on a display screen of the automatic teller machine, a requestto use a secondary input device; initiating a device session with thesecondary device; and receiving user inputs via the secondary device. 9.The method of claim 2, further comprising: in response to receiving theuser request, verifying an identify of user at the automatic tellermachine; and determining a user account associated with the user. 10.The method of claim 2, wherein the facial recognition model comprises amachine learning model trained to identify noses and faces in images.11. The method of claim 10, wherein the machine learning model isfurther trained to identify whether users are wearing masks.
 12. Anon-transitory computer-readable media implemented in an automaticteller machine comprising instructions that, when executed by one ormore processors, cause operations comprising: receiving a user requestto initiate a session with the automatic teller machine; capturing animage of a user at the automatic teller machine in response to receivingthe user request; processing the image using facial recognitiontechnology to identify a nose and a mouth of the user; in response toidentifying the nose or mouth in the image: determining that the user isnot in compliance with a hygienic standard; and executing a hygienicaction to cleanse the automatic teller machine; and determining that theuser is in compliance with the hygienic standard in response to notidentifying the nose or mouth in the image.
 13. The non-transitorycomputer-readable media of claim 12, wherein the hygienic actioncomprises initiating an ultraviolet sanitation procedure on theautomatic teller machine.
 14. The non-transitory computer-readable mediaof claim 12, wherein the hygienic action comprises generating fordisplay, on a display screen of the automatic teller machine, anotification to use an alternative automatic teller machine.
 15. Thenon-transitory computer-readable media of claim 12, wherein the hygienicaction comprises disabling the automatic teller machine from service.16. The non-transitory computer-readable media of claim 12, wherein thehygienic action comprises transmitting a sanitation service request toan automatic teller machine provider.
 17. The non-transitorycomputer-readable media of claim 12, wherein the hygienic actioncomprises initiating an alternative control scheme for the automaticteller machine, and wherein the alternative control scheme comprisesgesture-based control or voice activated controls.
 18. Thenon-transitory computer-readable media of claim 12, wherein the hygienicaction comprises initiating an alternative control scheme for theautomatic teller machine, and wherein initiating the alternative controlscheme comprises: generating for display, on a display screen of theautomatic teller machine, a request to use a secondary input device;initiating a device session with the secondary device; and receivinguser inputs via the secondary device.
 19. The non-transitorycomputer-readable media of claim 12, wherein the instructions furthercause operations comprising: in response to receiving the user request,verifying an identify of user at the automatic teller machine; anddetermining a user account associated with the user.
 20. Thenon-transitory computer-readable media of claim 12, wherein the facialrecognition model comprises a machine learning model trained to identifynoses and faces in images.